OpenRaiser/Intern-Atlas huggingface dataset:
ViTextVQA: A Large-Scale Visual Question Answering Dataset and a Novel Multimodal Feature Fusion Method for Vietnamese Text Comprehension in Images
Venue: arXiv cs.CL 2026
Year: 2026

Visual Question Answering (VQA) is a challenging task that requires the joint understanding of natural language and visual content. While early research primarily focused on recognizing objects and scene context, it often overlooked scene text-an essential source of explicit semantic information. This paper introduces \textbf{ViTextVQA} (\textbf{Vi}etnamese \textbf{Text}-based \textbf{V}isual \textbf{Q}uestion \textbf{A}nswering), the first large-scale Vietnamese dataset specializing in text-based VQA. The dataset contains \textbf{over 16,000} images and \textbf{over 50,000} question-answer pairs. To tackle this task efficiently, \textbf{ViTextBLIP-2} (Vietnamese Text-based Bootstrapped Language-Image Model via Fine-tuning) is proposed, a novel multimodal feature fusion method designed to optimize Vietnamese text-based VQA. Experiments with state-of-the-art models highlight the importance of token ordering in OCR text for answer generation, leading to significant performance improvements. The ViTextVQA dataset is publicly available for research purposes.
A Novel Convex Layers Strategy for Circular Formation in Multi-Agent Systems
Venue: arXiv cs.MA 2026
Year: 2026

This article considers the problem of conflict-free distribution of point-sized agents on a circular periphery encompassing all agents. The two key elements of the proposed policy include the construction of a set of convex layers (nested convex polygons) using the initial positions of the agents, and a novel search space region for each of the agents. The search space for an agent on a convex layer is defined as the region enclosed between the lines passing through the agent's position and normal to its supporting edges. Guaranteeing collision-free paths, a goal assignment policy designates a unique goal position within the search space of an agent at the initial time itself, requiring no further computation thereafter. In contrast to the existing literature, this work presents a one-shot, collision-free solution to the circular distribution problem by utilizing only the initial positions of the agents. Illustrative examples and extensive Monte-Carlo studies considering various practical attributes demonstrate the effectiveness of the proposed method.
Spectral Convolutional Conditional Neural Processes
Venue: arXiv cs.LG 2026
Year: 2026

Neural Processes (NPs) are meta-learning models that learn to map sets of observations to approximations of the corresponding posterior predictive distributions. By accommodating variable-sized, unstructured collections of observations and enabling probabilistic predictions at arbitrary query points, NPs provide a flexible framework for modeling functions over continuous domains. Since their introduction, numerous variants have emerged; however, early formulations shared a fundamental limitation: they compressed the observed data into finite-dimensional global representations via aggregation operations such as mean pooling. This strategy induces an intrinsic mismatch with the infinite-dimensional nature of the stochastic processes that NPs intend to model. Convolutional conditional neural processes (ConvCNPs) address this limitation by constructing infinite-dimensional functional embeddings processed through convolutional neural networks (CNNs) to enforce translation equivariance. Yet CNNs with local spatial kernels struggle to capture long-range dependencies without resorting to large kernels, which impose significant computational costs. To overcome this limitation, we propose spectral ConvCNPs (SConvCNPs), which perform global convolution in the frequency domain. Inspired by Fourier neural operators (FNOs) for learning solution operators of partial differential equations (PDEs), our approach directly parameterizes convolution kernels in the frequency domain, leveraging the relatively compact yet global Fourier representation of many natural signals. We validate the effectiveness of SConvCNPs on both synthetic and real-world datasets, demonstrating how ideas from operator learning can advance the capabilities of NPs.
Optimal Design for Human Preference Elicitation
Venue: arXiv cs.LG 2026
Year: 2026

Learning of preference models from human feedback has been central to recent advances in artificial intelligence. Motivated by the cost of obtaining high-quality human annotations, we study efficient human preference elicitation for learning preference models. The key idea in our work is to generalize optimal designs, an approach to computing optimal information-gathering policies, to lists of items that represent potential questions with answers. The policy is a distribution over the lists and we elicit preferences from them proportionally to their probabilities. To show the generality of our ideas, we study both absolute and ranking feedback models on items in the list. We design efficient algorithms for both and analyze them. Finally, we demonstrate that our algorithms are practical by evaluating them on existing question-answering problems.
A Differential and Pointwise Control Approach to Reinforcement Learning
Venue: arXiv cs.LG 2026
Year: 2026

Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing due to poor sample efficiency and lack of pathwise physical consistency. We introduce Differential Reinforcement Learning (Differential RL), a novel framework that reformulates RL from a continuous-time control perspective via a differential dual formulation. This induces a Hamiltonian structure that embeds physics priors and ensures consistent trajectories without requiring explicit constraints. To implement Differential RL, we develop Differential Policy Optimization (dfPO), a pointwise, stage-wise algorithm that refines local movement operators along the trajectory for improved sample efficiency and dynamic alignment. We establish pointwise convergence guarantees, a property not available in standard RL, and derive a competitive theoretical regret bound of $\mathcal{O}(K^{5/6})$. Empirically, dfPO outperforms standard RL baselines on representative scientific computing tasks, including surface modeling, grid control, and molecular dynamics, under low-data and physics-constrained conditions.
Low-Rank Online Dynamic Assortment with Dual Contextual Information
Venue: arXiv cs.IR 2026
Year: 2026

As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features. In high-dimensional scenarios, the quadratic growth of dimensions complicates computation and estimation. To tackle this challenge, we introduce a new low-rank dynamic assortment model to transform this problem into a manageable scale. Then we propose an efficient algorithm that estimates the intrinsic subspaces and utilizes the upper confidence bound approach to address the exploration-exploitation trade-off in online decision making. Theoretically, we establish a regret bound of $\tilde{O}((d_1+d_2)r\sqrt{T})$, where $d_1, d_2$ represent the dimensions of the user and item features respectively, $r$ is the rank of the parameter matrix, and $T$ denotes the time horizon. This bound represents a substantial improvement over prior literature, achieved by leveraging the low-rank structure. Extensive simulations and an application to the Expedia hotel recommendation dataset further demonstrate the advantages of our proposed method.
A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
Venue: arXiv cs.RO 2026
Year: 2026

Reinforcement learning has emerged as an important approach for autonomous driving. A reward function is used in reinforcement learning to establish the learned skill objectives and guide the agent toward the optimal policy. Since autonomous driving is a complex domain with partly conflicting objectives with varying degrees of priority, developing a suitable reward function represents a fundamental challenge. This paper aims to highlight the gap in such function design by assessing different proposed formulations in the literature and dividing individual objectives into Safety, Comfort, Progress, and Traffic Rules compliance categories. Additionally, the limitations of the reviewed reward functions are discussed, such as objectives aggregation and indifference to driving context. Furthermore, the reward categories are frequently inadequately formulated and lack standardization. This paper concludes by proposing future research that potentially addresses the observed shortcomings in rewards, including a reward validation framework and structured rewards that are context-aware and able to resolve conflicts.
$\aleph$-IPOMDP: Mitigating Deception in a Cognitive Hierarchy with Off-Policy Counterfactual Anomaly Detection
Venue: arXiv cs.MA 2026
Year: 2026

Social agents with finitely nested opponent models are vulnerable to manipulation by agents with deeper recursive capabilities. This imbalance, rooted in logic and the theory of recursive modelling frameworks, cannot be solved directly. We propose a computational framework called $\aleph$-IPOMDP, which augments the Bayesian inference of model-based RL agents with an anomaly detection algorithm and an out-of-belief policy. Our mechanism allows agents to realize that they are being deceived, even if they cannot understand how, and to deter opponents via a credible threat. We test this framework in both a mixed-motive and a zero-sum game. Our results demonstrate the $\aleph$-mechanism's effectiveness, leading to more equitable outcomes and less exploitation by more sophisticated agents. We discuss implications for AI safety, cybersecurity, cognitive science, and psychiatry.
Are Biological Systems More Intelligent Than Artificial Intelligence?
Venue: arXiv cs.AI 2026
Year: 2026

Are biological self-organising systems more ``intelligent'' than artificial intelligence (AI)? If so, why? I address this question using a mathematical framework that defines intelligence in terms of adaptability. Systems are modelled as stacks of abstraction layers (\emph{Stack Theory}) and compared by how effectively they delegate agentic control down their stacks. I illustrate this using computational, biological, military, governmental, and economic systems. Contemporary AI typically relies on static, human-engineered stacks whose lower layers are fixed during deployment. Put provocatively, such systems resemble inflexible bureaucracies that adapt only top-down. Biological systems are more intelligent because they delegate adaptation. Formally, I prove a theorem (\emph{The Law of the Stack}) showing that adaptability at higher layers is bottlenecked by adaptability at lower layers. I further show that, under standard viability assumptions, maximising adaptability is equivalent to minimising variational free energy, implying that delegation is necessary for free-energy minimisation. Generalising bioelectric accounts of cancer as isolation from collective informational structures, I analyse cancer-like failure modes in non-biological systems when delegation is inadequate. This yields design principles for building robust systems via delegated control, and reframes hybrid agents (e.g. organoids or human--AI systems) as weak boundary-condition design problems in which constraints shape low-level policy spaces while preserving collective identity.
Improved All-Pairs Approximate Shortest Paths in Congested Clique
Venue: arXiv cs.DS 2026
Year: 2026

In this paper, we present a new randomized $O(1)$-approximation algorithm for the All-Pairs Shortest Paths (APSP) problem in weighted undirected graphs that runs in just $O(\log \log \log n)$ rounds in the Congested-Clique model. Before our work, the fastest algorithms achieving an $O(1)$-approximation for APSP in weighted undirected graphs required $\operatorname{poly}(\log n)$ rounds, as shown by Censor-Hillel, Dory, Korhonen, and Leitersdorf (PODC 2019 & Distributed Computing 2021). In the unweighted undirected setting, Dory and Parter (PODC 2020 & Journal of the ACM 2022) obtained $O(1)$-approximation in $\operatorname{poly}(\log \log n)$ rounds. By terminating our algorithm early, for any given parameter $t \geq 1$, we obtain an $O(t)$-round algorithm that guarantees an $O\left(\log^{1/2^t} n\right)$ approximation in weighted undirected graphs. This tradeoff between round complexity and approximation factor offers flexibility, allowing the algorithm to adapt to different requirements. In particular, for any constant $\varepsilon > 0$, an $O\left(\log^\varepsilon n\right)$-approximation can be obtained in $O(1)$ rounds. Previously, $O(1)$-round algorithms were only known for $O(\log n)$-approximation, as shown by Chechik and Zhang (PODC 2022). A key ingredient in our algorithm is a lemma that, under certain conditions, allows us to improve an $a$-approximation for APSP to an $O(\sqrt{a})$-approximation in $O(1)$ rounds. To prove this lemma, we develop several new techniques, including an $O(1)$-round algorithm for computing the $k$-nearest nodes, as well as new types of hopsets and skeleton graphs based on the notion of $k$-nearest nodes.
An Improved Reversible Data Hiding Algorithm Based on Reconstructed Mapping for PVO-k
Venue: arXiv cs.CR 2026
Year: 2026

Reversible Data Hiding (RDH) is a practical and efficient technique for information encryption. Among its methods, the Pixel-Value Ordering (PVO) algorithm and its variants primarily modify prediction errors to embed information. However, both the classic PVO and its improved versions, such as IPVO and PVO-k, share a common limitation: their maximum data embedding capacity for a given grayscale image is relatively low. This poses a challenge when large amounts of data need to be embedded into an image. In response to these issues, this paper proposes an improved design targeting the PVO-k algorithm. We have reconstructed the mapping scheme of the PVO-k algorithm to maximize the number of pixels that can embed encrypted information. Experimental validations show that our proposed scheme significantly surpasses previous algorithms in terms of the maximum data embedding capacity. For instance, when embedding information into a grayscale image of an airplane, our method's capacity exceeds that of PVO-k by 11,207 bits, PVO by 8,004 bits, and IPVO by 4,562 bits. The results demonstrate that our algorithm holds substantial advantages over existing methods and introduces innovative mapping ideas, laying a foundation for future research in reversible data hiding in images.
Data-driven Error Estimation: Excess Risk Bounds without Class Complexity as Input
Venue: arXiv cs.LG 2026
Year: 2026

Constructing confidence intervals that are simultaneously valid across a class of estimates is central to tasks such as multiple mean estimation, generalization guarantees, and adaptive experimental design. We frame this as an ``error estimation problem," where the goal is to determine a high-probability upper bound on the maximum error for a class of estimates. We propose an entirely data-driven approach that derives such bounds for both finite and infinite class settings, naturally adapting to a potentially unknown correlation structure of random errors. Notably, our method does not require class complexity as an input, overcoming a major limitation of existing approaches. We present our simple yet general solution and demonstrate applications to simultaneous confidence intervals, excess-risk control and optimizing exploration in contextual bandit algorithms.
Leveraging Content Producer Networks and User Perception to Detect Online Discursive Communities
Venue: arXiv cs.SI 2026
Year: 2026

Online discussions are often characterized by strong behavioral asymmetries: a relatively small fraction of users actively produces content, while the majority primarily consumes and redistributes it. Here we propose a community-detection framework for online social networks that exploits this asymmetry by first identifying and clustering a set of leading users, and then extending the resulting labels to the broader user base. We introduce two complementary strategies to cluster leaders, one based on their mutual interactions and the other on audience overlap, both relying on entropy-based filtering to separate signal from noise. We evaluate the framework on three major Italian political debates on Twitter/X, using public figures--identified through the pre-2022 verification system--as leaders, and known affiliations of political actors as ground truth labels. Compared with standard baselines, the proposed approach yields more coherent and interpretable communities aligned with political structures, with the two variants respectively recovering parties and coalitions. Activity-based criteria for selecting leaders produce qualitatively similar but consistently weaker results, particularly at the coalition level. Overall, our findings show that creating statistically validated networks of publicly recognized figures, whose off-platform roles constrain and stabilize their online behavior, provide a strong basis to identify discursive communities on social media. Although developed for Twitter/X, the approach is conceptually general, as it leverages structural asymmetries common to many online platforms.
Computational lexical analysis of Flamenco genres
Venue: arXiv cs.CL 2026
Year: 2026

Flamenco, recognized by UNESCO as part of the Intangible Cultural Heritage of Humanity, is a profound expression of cultural identity rooted in Andalusia, Spain. However, there is a lack of quantitative studies that help identify characteristic patterns in this long-lived music tradition. In this work, we present a computational analysis of Flamenco lyrics, employing natural language processing and machine learning to categorize over 2000 lyrics into their respective Flamenco genres, termed as $\textit{palos}$. Using a Multinomial Naive Bayes classifier, we find that lexical variation across styles enables to accurately identify distinct $\textit{palos}$. More importantly, from an automatic method of word usage, we obtain the semantic fields that characterize each style. Further, applying a metric that quantifies the inter-genre distance we perform a network analysis that sheds light on the relationship between Flamenco styles. Remarkably, our results suggest historical connections and $\textit{palo}$ evolutions. Overall, our work illuminates the intricate relationships and cultural significance embedded within Flamenco lyrics, complementing previous qualitative discussions with quantitative analyses and sparking new discussions on the origin and development of traditional music genres.
Wall-Street: An Intelligent Vehicular Surface for Reliable mmWave Handover
Venue: arXiv cs.NI 2026
Year: 2026

mmWave networks promise high bandwidth but face significant challenges in maintaining reliable connections for users moving at high speed. Frequent handovers, complex beam alignment, and signal blockage from car bodies lead to service interruptions and degraded performance. We present Wall-Street, a vehicle-mounted smart surface that enhances mmWave connectivity for in-vehicle users. Wall-Street improves mobility management by (1) steering outdoor mmWave signals into the vehicle for shared coverage and providing a single, collective handover for all users; (2) performing neighbor-cell search without interrupting data transfer, ensuring seamless handovers; and (3) connecting users to a new cell before disconnecting from the old cell for reliable cell transitions. We implemented and integrated Wall-Street into the COSMOS testbed. We collected PHY traces with multiple base station nodes and in-vehicle user nodes with a surface-mounted vehicle, driving on a nearby road. Our trace-driven ns-3 simulation demonstrates a throughput im- provement of up to 78% and a latency reduction of up to 34% over the standard Standalone handover scheme.
The computational power of discrete chemical reaction networks with bounded executions
Venue: arXiv cs.CC 2026
Year: 2026

Chemical reaction networks (CRNs) model systems where molecules interact according to a finite set of reactions such as $A + B \to C$, representing that if a molecule of $A$ and $B$ collide, they disappear and a molecule of $C$ is produced. CRNs can compute Boolean-valued predicates $ϕ:\mathbb{N}^d \to \{0,1\}$ and integer-valued functions $f:\mathbb{N}^d \to \mathbb{N}$; for instance $X_1 + X_2 \to Y$ computes the function $\min(x_1,x_2)$. We study the computational power of execution bounded CRNs, in which only a finite number of reactions can occur from the initial configuration (e.g., ruling out reversible reactions such as $A \rightleftharpoons B$). The power and composability of such CRNs depend crucially on some other modeling choices that do not affect the computational power of CRNs with unbounded executions, namely whether an initial leader is present, and whether (for predicates) all species are required to "vote" for the Boolean output. If the CRN starts with an initial leader, and can allow only the leader to vote, then all semilinear predicates and functions can be stably computed in $O(n \log n)$ parallel time by execution bounded CRNs. However, if no initial leader is allowed, all species vote, and the CRN is "noncollapsing" (does not shrink from initially large to final $O(1)$ size configurations), then execution bounded CRNs are severely limited, able to compute only eventually constant predicates. A key tool is to characterize execution bounded CRNs as precisely those with a nonnegative linear potential function that is strictly decreased by every reaction, a result that may be of independent interest.
SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
Venue: arXiv cs.LG 2026
Year: 2026

In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are trained and communicated each round between the clients and the server. On standard benchmarks including CIFAR-10, CIFAR-100, and Tiny-ImageNet, SSFL consistently improves the accuracy sparsity trade off, achieving more than 20\% relative error reduction on CIFAR-10 compared to the strongest sparse baseline, while reducing communication costs by $2 \times$ relative to dense FL. Finally, in a real-world federated learning deployment, SSFL delivers over $2.3 \times$ faster communication time, underscoring its practical efficiency.